Investigation of the hydrodynamic response of cells in drop on demand piezoelectric inkjet nozzles
Bibliographic record
Abstract
Cell motion within a liquid suspension inside a piezoelectrically actuated, cylindrical inkjet printhead was studied using high speed imaging and a low depth of field setup. For each ejected droplet, a cell within the inkjet nozzle was observed to exhibit one of three possible behaviors which are termed: cell travel, cell ejection and cell reflection. Cell reflection is an undesirable phenomenon which may adversely affect an inkjet's capability in dispensing cells and a possible reason why it was previously reported that the rate of cells dispensed did not follow the expected Poisson distribution. Through the study of the cells motions, it was hypothesized that the rheological properties of the media in the cell suspension play an important role in influencing the cell behaviors exhibited. This was experimentally studied with the tracking of cells within the inkjet nozzle in a 10% w/v Ficoll PM400 cell suspension. The effect of cell reflection was eliminated using the higher density and viscosity Ficoll PM400 suspension. The presented work is the first in-depth study of the cell behaviors occurring within a piezoelectric inkjet nozzle during the printing process. The understanding of the hydrodynamics during a droplet ejection and its effect on the suspended cells are imperative towards achieving reliable cell dispensing for biofabrication applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".